PROJECT SUMMARY/ABSTRACT Patients with asymptomatic aortic valve stenosis (AS) patients have a high mortality risk. Treatment by aortic valve replacement (AVR) is an effective therapy but requires timely identification of the appropriate condition. The current approach for determining symptomatic status is highly subjective and often fails to indicate intervention before irreversible cardiac damage occurs. Similarly, leaving at-risk asymptomatic patients untreated may also result in their developing the adverse myocardial sequellae of AS. Evaluation of cardiac ventricular morphology, global and regional cardiac function, aortic compliance, as well as myocardial tissue composition would provide a comprehensive assessment of the heart health in patients with AS that could track the remodeling of the LV as a result of chronic sustained aortic valve pressure overload. In this project, we propose to develop three categories of magnetic resonance imaging (MRI) approaches for comprehensively and quantitatively providing the desired evaluation on the heart health. They are: 1) 3D CINE MRI for structural and functional evaluation; 2) 4D flow MRI for myocardial regional function and blood flow assessment; and 3) 3D quantitative parametric mapping for tissue characterization, detecting myocardial fibrosis. They are highly accelerated free-breathing whole heart and aorta imaging techniques, which are well suitable for this fragile AS patient population who needs rapid acquisition, free breathing strategies, and no requirement of contrast administration. From the quantitative MRI measurements such as left ventricular ejection fraction (LVEF), LV hypertrophy, LV diastolic and systolic dysfunction, myocardial fibrosis, and aortic stiffness and distensibility, their association with severity of symptoms will be analyzed and best biomarkers for predicting symptomatic progression will be identified. This comprehensive quantitative MRI protocol for evaluation of myocardial tissue composition and function, and aortic abnormalities in patients with severe aortic valve stenosis will more accurately assess the effects of AS. More precisely, quantitative MR parameters may help predict development of symptoms and progression of symptom severity in patients with critical aortic stenosis, and improve current patient selection strategies for aortic valve replacement. Success of this project will have a strong impact on the health care system given the clinical importance of severe aortic stenosis, and the inability of current methods to reliably predict the development or progression of symptoms.